As more organizations embrace AI, automation and Big Data, how many are mature enough to really develop and leverage these innovative technologies?
By Alan Morrison, Sr. Research Fellow, Emerging Tech at PwC
Most companies can work with data at a small scale on one-off projects and achieve limited success. But the real question is, how can they work with data at scale on a continual, day-to-day basis? Without continual flows of the right data to the right place, how can they transform their operations and achieve true competitive advantage?
Organizations need to start working with data flows, not just in batch mode. Rigid organizations are trapped in single-mode thinking, while their most able competitors are creative, non-linear thinkers who leverage a diversity of views and approaches to achieve business goals.
Data is the lifeblood of any sizable organization today, and competitive advantage hinges on the ability of companies to harness machines to develop and act on insights to effect considerable performance improvements and other beneficial outcomes. To uncover those insights, companies need a continual flow of the right data to the right parts of the organization in the right format.
Aligning technology with business goals
Companies who succeed in their technology projects use knowledge-based decision making in an iterative development process and do not follow the herd. They’re creative, non-linear thinkers. They take the trouble to understand the problem deeply first. They often diagnose problems correctly and know enough to explore new tools when crafting a solution. If they run into trouble, they revisit their thinking and make corrections and refinements. They’re agile in their thinking as well as their actions. They infuse their thinking into the organizational culture. PwC for instance always starts with the human element. Many times, the biggest challenge isn’t the technology — it’s changing the organization and the process in a humanistic, sustainable way.
GDPR and Privacy: How much data do you need?
We’re all engaged in a struggle to enrich data and use it more effectively. Enriching data implies identifying more and more relationships between people, places and things. Quickly you get into the realm of data privacy. The GDPR imposes some helpful requirements, and we clearly need to be advocates for individuals to be able to control their most sensitive data. But the devil’s always in the details.
Take machine vision in cars, for example — Sandra Wachter of Oxford has pointed out that data used by cars to navigate through obstacles and traffic can also be used for to identify specific individuals, via image data that can be considered biometric. How we manage such data continues to be an open question.
Market Evolution and spotting technology trends
Organizational boundaries are becoming more porous, and there’s more and more collaboration between organizations. We’ve also seen the rise of the gig economy — freelancers or contractors are more in evidence. In some cases, the bulk of the entire organization consists of contractors. In general, we’re just seeing a more fluid environment. IDC describes the online working environment as the Innovation Graph. Companies will need to consider how to position themselves in new roles in this Graph. Companies can morph into new roles this way and do their own boundary crossing in the process.
Why companies should pay attention to semantic technology
Graphs and how they can represent connections between people, places and things can articulate and scale all our knowledge of how the world works, in a machine readable form. Just think about how powerful those graphs can be. Those articulated connections, the bridge between human and machine knowledge, can be called semantic graphs.
To get to scale and business model agility, companies need to create a semantic graph foundation for AI. Semantic graphs might be seen as the parent data structure that can manage all the children, because they allow full contextualization of disparate data types and machine readable articulation of the rich relationships that need to be mined in any organization.
Relationship, not relational, data is what allows us to disambiguate and describe each context. Just ask a social media company — what’s more powerful than a graph to describe and better articulate customer relationships and all the segments and subsegments of the markets serving those customers? Just ask any fraud investigator — what’s a more powerful way than graphs to find bad actors?
Graphs as the parents, the most articulated data models, can easily incorporate less articulated models such as tables and documents — the children. That’s how large-scale integration happens. Business users need to think in terms of graphs much more often, and not just in terms of tables. Companies are hobbling themselves if they can’t get beyond tabular data models. Graphs hold the power of large-scale integration.
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Alan Morrison is a keynote speaker at SEMANTiCS 2018. His keynote will address how technology innovation and especially the adoption of AI impacts new business models.
Originally published at Tech Trends.